{"title":"The Influence of Additional Virtual Synchronous Generator Technology in VSC-MTDC Systems with Wind Power on System Frequency","authors":"Congshan Li, Kefeng Zhao, Ping He, Zikai Zhen","doi":"10.2174/0118722121294488240223075517","DOIUrl":"https://doi.org/10.2174/0118722121294488240223075517","url":null,"abstract":"\u0000\u0000A frequency control strategy is proposed based on additional virtual synchronous\u0000generator technology for voltage source converter-based multi-terminal high voltage direct\u0000current systems with wind power.\u0000\u0000\u0000\u0000This strategy addresses the system's inertia reduction and frequency stability issues caused\u0000by integrating large amounts of wind power through multi-terminal DC transmission. Firstly, the\u0000virtual synchronous generator mathematical model is constructed based on the system structure. Secondly,\u0000for the problem of zero rotational inertia of voltage source converter in a flexible DC transmission\u0000system, based on the P-U droop control method of the converter station, additional virtual\u0000synchronous control generation technology is applied to simulate the P-f droop characteristics of the\u0000synchronous generator by adding virtual rotational inertia, so that the converter has the inertial response\u0000of synchronous generator to realize primary frequency regulation.\u0000\u0000\u0000\u0000Finally, the simulation is verified on the PSCAD/ EMTDC platform with an example of a\u0000three-terminal parallel MTDC transmission system.\u0000\u0000\u0000\u0000the analyzed results demonstrate that the virtual synchronous generator control strategy\u0000is very valuable and useful for improving the frequency performance of the system.\u0000","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Secure Vehicle-to-Vehicle Communication Using Routing Protocol Based On Trust Authentication Secure Sugeno Fuzzy Inference System Scheme","authors":"Anupama K N, R. Nagaraj","doi":"10.2174/0118722121269253240214075231","DOIUrl":"https://doi.org/10.2174/0118722121269253240214075231","url":null,"abstract":"\u0000\u0000Vehicular Ad-hoc Network (VANET) is wireless communication between\u0000Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising\u0000technology that effectively manages traffic and ensures road safety. However, communication in an\u0000open-access environment presents real challenges to security and privacy issues, which may affect\u0000large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and\u0000communication are the most challenging areas in previous methods. Vehicular communications face\u0000challenges due to vehicle interference and severe delays.\u0000\u0000\u0000\u0000To overcome the drawbacks, this work proposed a new method based on the Artificial Neural\u0000Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc\u0000Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators.\u0000Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust\u0000Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the\u0000security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating\u0000the node weights for data transferring; reduced the attacks accuracy of network malicious attacks.\u0000\u0000\u0000\u0000To overcome the drawbacks, this work proposed a new method based on Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET.\u0000\u0000\u0000\u0000In the improved cluster-based VANET routing protocol, each node obtains an address using a\u0000new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside\u0000Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation\u0000term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission.\u0000\u0000\u0000\u0000Simulation results show that in the proposed ANN-based Trust Authentication Secure\u0000Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance\u0000is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and\u0000the transmission ratio is 89%, attack detection is 90%.\u0000","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140437926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Singha, Barsha Panda, Syed Benazir Firdaus, D. Ghosh
{"title":"Machine Learning (ML) Techniques in Healthcare Systems: A Mini Review","authors":"P. Singha, Barsha Panda, Syed Benazir Firdaus, D. Ghosh","doi":"10.2174/0118722121291771240216044918","DOIUrl":"https://doi.org/10.2174/0118722121291771240216044918","url":null,"abstract":"\u0000\u0000Artificial intelligence (AI) has made its own place in the present world. Almost in every\u0000field, AI is being utilized for betterment and advancement. Machine learning (ML) is a part of AI\u0000and has been applied extensively currently in various fields of science and technology including\u0000healthcare system. ML is the technique that uses AI to analyze, interpret and make decisions.\u0000To summarize the applications of ML in various healthcare systems in order to understand the\u0000strength and loopholes of the use of ML in medical science.\u0000The mechanisms and methods of ML approach in various medical issues have been analyzed and\u0000discussed. ML technique is being used to make decisions in medical cases, for determining the\u0000treatment regime of a particular patient, for designing and developing drugs, in personalized medicine,\u0000in designing and selecting diagnoses for any particular disease, for automated tracking of patient's\u0000recovery. Available clinical data and history are being used by ML techniques to compare,\u0000classify, select and execute results for any task being assigned. In a nutshell, ML uses earlier available\u0000information and data about the disease, the treatment protocols followed, and the results in correspondence\u0000with the clinical symptoms and pathological findings.\u0000Several achievements using ML in the healthcare system, yielded significant novel results that have\u0000been patented. There have been several thousand patents in the field of application of ML in\u0000healthcare systems from the years 2012 to 2023.\u0000Though, ML in healthcare comes with some risks and unknown possibilities yet, restricted and monitored\u0000application of ML in healthcare may hasten the healthcare system, save time, help to make\u0000efficient decisions in non-invasive ways, and may open up new possibilities in the healthcare system.\u0000","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140440840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Review on Detection and Prevention Techniques of Scripting Attacks:\u0000Gaps, Challenges and Suggestions","authors":"Seema Sharma, Narendra Singh Yadav","doi":"10.2174/0118722121293163240212030405","DOIUrl":"https://doi.org/10.2174/0118722121293163240212030405","url":null,"abstract":"\u0000\u0000Web apps hold important information, such as login tokens and individual data, and cybercriminals\u0000repeatedly target attackers. Cross-site scripting is one of the most frequent vulnerabilities\u0000in web apps. Several techniques and patents are used to mitigate these vulnerabilities. Several\u0000100 articles from a review of research papers published between 2005 and 2023 were considered.\u0000This paper reviewed different techniques and tools to detect cross-site scripting attacks, and it will be\u0000helpful to understand, analyze, and develop a strategy to deal with them. This paper focuses on different\u0000methods and tools for identifying cross-site scripting (XSS) attacks. Also, it depicts the\u0000strengths and shortcomings of the existing proposed method. Additionally, it will help to understand\u0000existing open issues or challenges faced by previous researchers.\u0000","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}